20 citations · 45 across the 23 of their papers we have counts for
8 papers · 1 filter
Skelite: Compact Neural Networks for Efficient Iterative Skeletonization
Luis D. Reyes Vargas, Martin J. Menten, Johannes C. Paetzold +2
Skeletonization extracts thin representations from images that compactly encode their geometry and topology. These representations have become an important topological prior for pr…
Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation
Marina Domínguez, Yordanka Velikova, Nassir Navab +1
Deep learning (DL) methods typically require large datasets to effectively learn data distributions. However, in the medical field, data is often limited in quantity, and acquiring…
Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
Miruna-Alexandra Gafencu, Yordanka Velikova, Mahdi Saleh +4
Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D infor…
LOTUS: Learning to Optimize Task-based US representations
Yordanka Velikova, Mohammad Farid Azampour, Walter Simson +2
Anatomical segmentation of organs in ultrasound images is essential to many clinical applications, particularly for diagnosis and monitoring. Existing deep neural networks require…
Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging
Magdalena Wysocki, Mohammad Farid Azampour, Christine Eilers +3
We present a physics-enhanced implicit neural representation (INR) for ultrasound (US) imaging that learns tissue properties from overlapping US sweeps. Our proposed method leverag…
CACTUSS: Common Anatomical CT-US Space for US examinations
Yordanka Velikova, Walter Simson, Mehrdad Salehi +3
Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has b…